In this paper, we study the codebook-based near-field beam training for intelligent reflecting surfaces (IRSs) aided wireless system. In the considered model, the near-field beam training is critical to focus signals at the location of user equipment (UE) to obtain prominent IRS array gain. However, existing codebook schemes cannot achieve low training overhead and high receiving power simultaneously. To tackle this issue, a novel two-layer codebook based beam training scheme is proposed. The layer-1 codebook is designed based on the omnidirectionality of a random-phase beam pattern, which estimates the UE distance with training overhead equivalent to that of one DFT codeword. Then, based on the estimated UE distance, the layer-2 codebook is generated to scan candidate UE locations and obtain the optimal codeword for IRS beamforming. Numerical results show that compared with benchmarks, the proposed two-layer beam training scheme achieves more accurate UE distance and angle estimation, higher data rate, and smaller training overhead.
翻译:在本文中,我们研究了智能反射面(IRS)辅助的无线系统中基于码本的近场波束训练。在所考虑的模型中,近场波束训练对于将信号集中在用户设备(UE)的位置以获得突出的IRS阵列增益至关重要。然而,现有的码本方案不能同时实现低培训开销和高接收功率。为了解决这个问题,提出了一种新的基于两层码本的波束训练方案。第一层码本是基于随机相位波束图的全向性设计的,它用培训开销相当于一个DFT码字来估计UE距离。然后,基于估计的UE距离,生成第二层码本来扫描候选的UE位置并获得用于IRS波束成形的最优码字。数值结果表明,与基准相比,所提出的两层波束训练方案实现了更准确的UE距离和角度估计,更高的数据速率和更小的训练开销。